A control strategy to investigate the relationship between specific productivity and high-mannose glycoforms in CHO cells.

The integration of physiological knowledge into process control strategies is a cornerstone for the improvement of biopharmaceutical cell culture technologies. The present contribution investigates the applicability of specific productivity as a physiological control parameter in a cell culture process producing a monoclonal antibody (mAb) in CHO cells. In order to characterize cell physiology, the on-line oxygen uptake rate (OUR) was monitored and the time-resolved specific productivity was calculated as physiological parameters. This characterization enabled to identify the tight link between the deprivation of tyrosine and the decrease in cell respiration and in specific productivity. Subsequently, this link was used to control specific productivity by applying different feeding profiles. The maintenance of specific productivity at various levels enabled to identify a correlation between the rate of product formation and the relative abundance of high-mannose glycoforms. An increase in high mannose content was assumed to be the result of high specific productivity. Furthermore, the high mannose content as a function of cultivation pH and specific productivity was investigated in a design of experiment approach. This study demonstrated how physiological parameters could be used to understand interactions between process parameters, physiological parameters, and product quality attributes.

Combining mechanistic and data-driven approaches to gain process knowledge on the control of the metabolic shift to lactate uptake in a fed-batch CHO process.

A growing body of knowledge is available on the cellular regulation of overflow metabolism in mammalian hosts of recombinant protein production. However, to develop strategies to control the regulation of overflow metabolism in cell culture processes, the effect of process parameters on metabolism has to be well understood. In this study, we investigated the effect of pH and temperature shift timing on lactate metabolism in a fed-batch Chinese hamster ovary (CHO) process by using a Design of Experiments (DoE) approach. The metabolic switch to lactate consumption was controlled in a broad range by the proper timing of pH and temperature shifts. To extract process knowledge from the large experimental dataset, we proposed a novel methodological concept and demonstrated its usefulness with the analysis of lactate metabolism. Time-resolved metabolic flux analysis and PLS-R VIP were combined to assess the correlation of lactate metabolism and the activity of the major intracellular pathways. Whereas the switch to lactate uptake was mainly triggered by the decrease in the glycolytic flux, lactate uptake was correlated to TCA activity in the last days of the cultivation. These metabolic interactions were visualized on simple mechanistic plots to facilitate the interpretation of the results. Taken together, the combination of knowledge-based mechanistic modeling and data-driven multivariate analysis delivered valuable insights into the metabolic control of lactate production and has proven to be a powerful tool for the analysis of large metabolic datasets.

Advanced Development Strategies for Biopharmaceutical Cell Culture Processes.

The shift from empirical to science-based process development is considered to be a key factor to increase bioprocess performance and to reduce time to market for biopharmaceutical products in the near future. In the last decade, expanding knowledge in systems biology and bioprocess technology has delivered the foundation of the scientific understanding of relationships between process input parameters and process output features. Based on this knowledge, advanced process development approaches can be applied to maximize process performance and to generate process understanding. This review focuses on tools which enable the integration of physiological knowledge into cell culture process development. As a structured approach, the availability and the proposed benefit of the application of these tools are discussed for the subsequent stages of process development. The ultimate aim is to deliver a comprehensive overview of the current role of physiological understanding during cell culture process development from clone selection to the scale-up of advanced control strategies for ensuring process robustness.

Ultrasound-enhanced attenuated total reflection mid-infrared spectroscopy in-line probe: acquisition of cell spectra in a bioreactor.

This article presents a novel method for selective acquisition of Fourier transform infrared (FT-IR) spectra of microorganisms in-line during fermentation, using Saccharomyces cerevisiae as an example. The position of the cells relative to the sensitive region of the attenuated total reflection (ATR) FT-IR probe was controlled by combing a commercially available ATR in-line probe with contact-free, gentle particle manipulation by ultrasonic standing waves. A prototype probe was successfully constructed, assembled, and tested in-line during fed-batch fermentations of S. cerevisiae. Control over the position of the cells was achieved by tuning the ultrasound frequency: 2.41 MHz was used for acquisition of spectra of the cells (pushing frequency f(p)) and 1.87 MHz, for retracting the cells from the ATR element, therefore allowing spectra of the medium to be acquired. Accumulation of storage carbohydrates (trehalose and glycogen) inside the cells was induced by a lack of a nitrogen source in the feed medium. These changes in biochemical composition were visible in the spectra of the cells recorded in-line during the application of f(p) and could be verified by reference spectra of dried cell samples recorded off-line with a FT-IR microscope. Comparison of the cell spectra with spectra of trehalose, glycogen, glucose, and mannan, i.e., the major carbohydrates present in S. cerevisiae, and principal components analysis revealed that the changes observed in the cell spectra correlated well with the bands specific for trehalose and glycogen. This proves the applicability and capability of ultrasound-enhanced in-line ATR mid-IR spectroscopy as a real-time PAT method for the in situ monitoring of cellular biochemistry during fermentation.

A dynamic method for the investigation of induced state metabolic capacities as a function of temperature.

BACKGROUND: Science-based recombinant bioprocess designs as well as the design of statistical experimental plans for process optimization (Design of Experiments, DoE) demand information on physiological bioprocess boundaries, such as the onset of acetate production, adaptation times, mixed feed metabolic capabilities or induced state maximum metabolic rates as at the desired cultivation temperature. Dynamic methods provide experimental alternatives to determine this information in a fast and efficient way. Information on maximum metabolic capabilities as a function of temperature is needed in case a reduced cultivation temperature is desirable (e.g. to avoid inclusion body formation) and an appropriate feeding profile is to be designed. RESULTS: Here, we present a novel dynamic method for the determination of the specific growth rate as a function of temperature for induced recombinant bacterial bioprocesses. The method is based on the control of the residual substrate concentration at non-limiting conditions with dynamic changes in cultivation temperature. The presented method was automated in respect to information extraction and closed loop control by means of in-line Fourier Transformation Infrared Spectroscopy (FTIR) residual substrate measurements and on-line first principle rate-based soft-sensors. Maximum induced state metabolic capabilities as a function of temperature were successfully extracted for a recombinant E. coli C41 fed-batch bioprocess without the need for sampling in a time frame of 20 hours. CONCLUSIONS: The presented method was concluded to allow the fast and automated extraction of maximum metabolic capabilities (specific growth rate) as a function of temperature. This complements the dynamic toolset necessary for science-based recombinant bacterial bioprocess design and DoE design.

Determination of carbohydrates present in Saccharomyces cerevisiae using mid-infrared spectroscopy and partial least squares regression.

A fast and simple method to control variations in carbohydrate composition of Saccharomyces cerevisiae, baker's yeast, during fermentation was developed using mid-infrared (mid-IR) spectroscopy. The method allows for precise and accurate determinations with minimal or no sample preparation and reagent consumption based on mid-IR spectra and partial least squares (PLS) regression. The PLS models were developed employing the results from reference analysis of the yeast cells. The reference analyses quantify the amount of trehalose, glucose, glycogen, and mannan in S. cerevisiae. The selection and optimization of pretreatment steps of samples such as the disruption of the yeast cells and the hydrolysis of mannan and glycogen to obtain monosaccharides were carried out. Trehalose, glucose, and mannose were determined using high-performance liquid chromatography coupled with a refractive index detector and total carbohydrates were measured using the phenol-sulfuric method. Linear concentration range, accuracy, precision, LOD and LOQ were examined to check the reliability of the chromatographic method for each analyte.

Model-based analysis on the extractability of information from data in dynamic fed-batch experiments.

Dynamic changes of physiological bioprocess parameters, e.g. a change in the specific growth rate µ, are frequently observed during industrial manufacturing as well as bioprocess development. A quantitative description of these variations is of great interest, since it can bring elucidation to the physiological state of the culture. The goal of this contribution was to show limitations and issues for the calculation of rates with regard to temporal resolution for dynamic fed-batch experiments. The impact of measurement errors, temporal resolution and the physiological activity on the signal to noise ratio (SNR) of the calculated rates was evaluated using an in-silico approach. To make use of that in practice, a generally applicable rule of thumb equation for the estimation of the SNR of specific rates was presented. The SNR calculated by this rule of thumb equation helps with definition of sampling intervals and making a decision whether an observed change is statistically significant or should be attributed to random error. Furthermore, a generic reconciliation approach to remove random as well as systematic error from data was presented. This reconciliation technique requires only little prior knowledge. The validity of the proposed tools was checked with real data from a fed-batch culture of E. coli with dynamic variations due to feed profile.

Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes.

The real-time measurement of biomass has been addressed since many years. The quantification of biomass in the induction phase of a recombinant bioprocess is not straight forward, since biological burden, caused by protein expression, can have a significant impact on the cell morphology and physiology. This variability potentially leads to poor generalization of the biomass estimation, hence is a very important issue in the dynamic field of process development with frequently changing processes and producer lines. We want to present a method to quantify "biomass" in real-time which avoids off-line sampling and the need for representative training data sets. This generally applicable soft-sensor, based on first principles, was used for the quantification of biomass in induced recombinant fed-batch processes. Results were compared with "state of the art" methods to estimate the biomass concentration and the specific growth rate µ. Gross errors such as wrong stoichiometric assumptions or sensor failure were detected automatically. This method allows for variable model coefficients such as yields in contrast to other process models, hence does not require prior experiments. It can be easily adapted to a different growth stoichiometry; hence the method provides good generalization, also for induced culture mode. This approach estimates the biomass (or anabolic bioconversion) in induced fed-batch cultures in real-time and provides this key variable for process development for control purposes.

Information Processing: Rate-Based Investigation of Cell Physiological Changes along Design Space Development.

Recent initiatives summarized under the term quality by design (QbD) urge for science and risk-based pharmaceutical bioprocess development strategies. One of the most accepted concepts communicated by the regulatory authorities is the concept of design space-a multidimensional combination of critical process parameter (CPP) ranges where the quality acceptance criteria (critical quality attributes, CQAs) are fulfilled. Current design space development along QbD principles focuses on the investigation of statistical CPP/CQA interactions, while the biological mechanistic of this interaction is hardly considered. Furthermore, the plethora of available online and offline data gathered within design space development is typically not used for the demonstration of process understanding. Here we present a methodology about how typical recorded process data can be processed and used to gather mechanistic process knowledge within upstream design space development, without the need for further experiments or additional analytical procedures. Data derived from online and offline measurements (off gas quantification, air flows, substrate flows, biomass dry cell weight measurements) were processed into scale-independent information in the form of specific rates and yield coefficients (data processing). Subsequently, the obtained information was regressed with the investigated process parameters aiming at the investigation of mechanistic interactions (information processing). The power of the presented approach was demonstrated on a multivariate study involving two process parameters (induction phase temperature and induction phase feeding strategy) aiming at the production of recombinant product in an Escherichia coli K12 strain. The knowledge successfully extracted indicated a time dependency of the metabolic load posed on the system, a possible down regulation of the promoter at reduced temperatures, and reduced cell lysis at higher specific feeding regimes. The presented data and information processing methodology for mechanistic process knowledge extraction is fully complementary to the task of design space development for QbD submissions and can serve as the basis of mechanistic modeling. LAY ABSTRACT: Manufacturing of pharmaceuticals intended for human use is under tight control of government authorities. To further improve product quality and allow more manufacturing flexibility, government agencies started to encourage manufactures to investigate and understand their manufacturing processes scientifically. This should lead to quality by design (QbD), hence a manufacturing that is so well understood that final product quality can be guaranteed by the manufacturing process itself.

Efficient feeding profile optimization for recombinant protein production using physiological information.

A multivariate study was performed aiming at the optimization of a recombinant rhamnose inducible E. coli induction system with alkaline phosphatase as target product. The effects of typical factors with impact on post- as well as pre-induction feeding rates were investigated with respect to the space-time yield of the target product. The goal was increased understanding as well as quantitative characterization of these factors with respect to their physiological impact on the model system. The optical density (OD) at which the culture was induced had a strong positive effect on the space-time yield. Pre-induction growth rate (k) had a second-order effect, while induction feed rate drop (J), a factor defining the linear post-induction feed rate, was interacting with (k). However, explanation of the observed effects to acquire more understanding regarding their effect on cell metabolism was not straight forward. Hence, the original process parameters were transformed into physiological more meaningful parameters and served as the basis for a multivariate data analysis. The observed variance with respect to observed volumetric activity was fully explained by the specific substrate uptake rate (q (s)) and induction OD, merging the process parameters pre-induction growth rate (k) and feed rate drop (J) into the physiological parameter specific substrate uptake rate (q (s)). After transformation of the response volumetric activity (U/ml) into the biomass specific activity (U/g(biomass)), the observed variance was fully explained solely by the specific substrate uptake rate (q (s)). Due to physiological multivariate data analysis, the interpretation of the results was facilitated and factors were reduced. On the basis of the obtained results, it was concluded that the physiological parameter q (s) rather than process parameters (k, J, induction OD) should be used for process optimization with respect to the feeding profile.

Model-based analysis on the relationship of signal quality to real-time extraction of information in bioprocesses.

Quality by design (QbD) is a current structured approach to design processes yielding a quality product. Knowledge and process understanding cannot be achieved without proper experimental data; hence requirements for measurement error and frequency of measurement of bioprocess variables have to be defined. In this contribution, a model-based approach is used to investigate impact factors on calculated rates to predict the obtainable information from real-time measurements (= signal quality). Measurement error, biological activity, and averaging window (= period of observation) were identified as biggest impact factors on signal quality. Moreover, signal quality has been set in context with a quantifiable measure using statistical error testing, which can be used as a benchmark for process analytics and exploitation of data. Results have been validated with data from an E. coli batch process. This approach is useful to get an idea which process dynamics can be observed with a given bioprocess setup and sampling strategy beforehand.